Spanish bilingual and Hispanic jobs since 1997. Diversity job fairs since 2006. employers     login   |   register - post a job
Hispanic Diversity Recruitment - best jobs for hispanic, latino & bilingual (spanish & portuguese) jobseekers
HOME
    Log me in!   |   Site Map   |   Help   
 Software Engineer - Applied Machine Learning, Ad Platforms - Cupertino, California, United States

   
Job information
Posted by: Apple 
Hiring entity type: Retail 
Work authorization: Not Specified for United States
Position type: Direct Hire, Full-Time 
Compensation: ******
Benefits: See below
Relocation: Not specified 
Position functions: Computers - Platforms
 
Travel: Unspecified 
Accept candidates: from anywhere 
Languages: English - Fluent
 
Minimum education: See below 
Minimum years experience: See below 
Resumes accepted in: English
Cover letter: No cover letter requested
Job code: 200229879 / Latpro-3778140 
Date posted: Mar-11-2021
State, Zip: California, 95014

Description

Software Engineer - Applied Machine Learning, Ad Platforms

Santa Clara Valley (Cupertino) , California , United States

Software and Services

Summary

Posted: Mar 11, 2021

Role Number: 200229879

At Apple, we work every day to create products that enrich people's lives. Our Advertising Platforms group makes it possible for people around the world to easily access informative and imaginative content on their devices while helping publishers and developers promote and monetize their work. Today, our technology and services power advertising in Search Ads in the App Store and Apple News. Our platforms are highly-performant, deployed at scale, and setting new standards for enabling effective advertising while protecting user privacy. We are looking for an ambitious individual who can thrive in an agile environment. You will develop distributed systems and apply cutting edge algorithms and ML models to improve relevance across a range of advertising applications. The position involved large scale data infrastructure building as well the capability to do big data analysis. Detecting meaningful data patterns; assuring the integrity and breadth of the data; measuring user, campaign and app performance; and finally analyzing the results of extremely large-scale experiments. In addition, the successful candidate will also apply advanced ML techniques for federated learning where privacy mechanisms are safeguarded at the very onset and delightful relevance experiences are built by applying encryption techniques, on-device segmentation, advanced language models, ranking algorithms by utilizing the best of aggregated server and on-device data.

Key Qualifications

  • 3+ years relevant experience in Algorithms, Architecture, Artificial Intelligence, Data Mining, Distributed Systems, Machine Learning, Networking, Statistics or Systems Software implementation of new algorithms
  • Practical understanding of the mathematics behind modern machine learning, linear algebra and statistics
  • Experience in Search, Advertising, Fraud prevention, Privacy-preserving ML highly desired
  • Ability to take requirements from design through to implementation both independently and with larger teams
  • Closely working with operational teams on deployment, monitoring, management concerns
  • Experience in distributed machine learning architectures and/or federated learning

Description

You will have the opportunity to work on a platform with extreme scale requirements. You should have experience developing and implementing relevant personalization algorithms, revenue optimization solutions and network data processes within a content + audience network. You have excellent understanding of scalable architectures including operational concerns. You have the ability to be a great teammate under tight deadline constraints is key to success.

Education & Experience

BS/MS/PhD in computer science or equivalent field



Requirements

See job description

 

Apple requires you to fill in their on-line form which will open in a different window.

Enter your email address and click 'Apply':
       Apply
  Prefer not to enter your email?